Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Neural Regulation01:37

Neural Regulation

34.8K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
34.8K
Neural Circuits01:25

Neural Circuits

3.0K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
3.0K
Spinal Cord: Information Processing01:10

Spinal Cord: Information Processing

4.2K
The spinal cord is an integral hub for motor and sensory information that enables the brain to communicate with the peripheral nervous system (PNS). This communication consists of relaying sensory data and transmission of motor commands.
Sensory Information Processing
Sensory information processing begins at the sensory receptors located in the skin and other tissues, which detect somatic sensory stimuli such as touch, temperature, or pain. These receptors function as catalysts, initiating...
4.2K
Neurons as Communicators of the Brain01:22

Neurons as Communicators of the Brain

5.3K
Neurons, the fundamental units of the brain and nervous system, function as the primary transmitters of information throughout the body. Their ability to communicate through electrical and chemical signals is vital for every bodily function, from regulating the heartbeat to processing complex thoughts. Each neuron has three main components: the cell body (soma), dendrites, and an axon, each specialized to facilitate swift and efficient neural communication.
Cell Body
The cell body, also known...
5.3K
Neuronal Communication01:28

Neuronal Communication

5.6K
Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
5.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

GMG-LDefmamba-YOLO: An Improved YOLOv11 Algorithm Based on Gear-Shaped Convolution and a Linear-Deformable Mamba Model for Small Object Detection in UAV Images.

Sensors (Basel, Switzerland)·2025
Same author

GaitRGA: Gait Recognition Based on Relation-Aware Global Attention.

Sensors (Basel, Switzerland)·2025
Same author

Mechanisms involved in ceramide-induced cell cycle arrest in human hepatocarcinoma cells.

World journal of gastroenterology·2007
Same author

A population-based survey of women's traditional postpartum behaviours in Northern China.

Midwifery·2007
Same author

A glimpse of streptococcal toxic shock syndrome from comparative genomics of S. suis 2 Chinese isolates.

PloS one·2007
Same author

Colon carcinoma cells harboring PIK3CA mutations display resistance to growth factor deprivation induced apoptosis.

Molecular cancer therapeutics·2007

Related Experiment Video

Updated: May 5, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

A Large Kernel Convolutional Neural Network with a Noise Transfer Mechanism for Real-Time Semantic Segmentation.

Jinhang Liu1,2, Yuhe Du1,2, Jing Wang1,2

  • 1School of Computer Science, Hubei University of Technology, Wuhan 430070, China.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
Summary

This study introduces LKNTNet, a novel semantic segmentation model that combines large kernel convolution with Atrous convolution. It achieves state-of-the-art accuracy and speed by adaptively capturing multi-scale features and improving generalization.

Keywords:
computer visionlarge kernel convolutionnoise transfer mechanismposition awarenessreal-time semantic segmentation

Related Experiment Videos

Last Updated: May 5, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

Area of Science:

  • Computer Vision
  • Deep Learning
  • Image Segmentation

Background:

  • Large kernels and Atrous convolution increase receptive fields in semantic segmentation.
  • Fixed kernel sizes limit adaptive multi-scale feature capture and global context utilization.

Purpose of the Study:

  • To enhance semantic segmentation models by overcoming limitations of fixed kernel sizes.
  • To improve adaptive multi-scale feature extraction and global contextual information leveraging.

Main Methods:

  • Combining Atrous convolution with large kernel convolution using varied dilation rates.
  • Implementing a dynamic selection mechanism for adaptive spatial feature highlighting.
  • Proposing a Multi-Scale Contextual Noise Transfer Matrix (NTM) for supervision signal correction.

Main Results:

  • LKNTNet achieves state-of-the-art speed and accuracy on benchmark datasets.
  • Achieved 80.05% mIoU on Cityscapes (80.7 FPS) and 42.7% mIoU on ADE20K (143.6 FPS).

Conclusions:

  • The proposed method effectively addresses multi-scale feature limitations in large kernel networks.
  • LKNTNet demonstrates superior generalization capability and performance in semantic segmentation tasks.